WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Speciation of Inorganic and Organometallic Arsenic in Various Matrices With a Novel Spme Fiber Functionalized With Iron Nanoparticles Prior To Lc-Icp Determination
    (Elsevier, 2025) Boyaci, Ezel; Cagir, Ali; Shahwan, Talal; Eroglu, Ahmet E.
    A novel SPME-LC-ICP-MS methodology is described for the simultaneous microextraction/speciation/determination of the metabolically critical inorganic and organoarsenic species, namely, As(III), As(V), dimethylarsinic acid (DMA), and monomethylarsonic acid (MMA) in natural waters such as drinking and geothermal waters, and biological fluids such as urine. The novelty of the study stems also from the use of home-made SPME fibers for the extraction process, and from the proposed methodology needing no derivatization step. SPME fibers were prepared with in-tube capillary template approach through the immobilization of iron nanoparticles into agarose matrix. The fibers demonstrated reproducible extraction (<10 % RSD), good mechanical strength and good solvent resistivity. The separation of the analytes was realized by HPLC with a strong anion exchange column via gradient elution using different concentrations of (NH4)(2)CO3 (pH 8.50), and the on-line detection of eluted analytes was achieved by ICP-MS. The validity of the proposed methodology was verified via the analysis of certified reference materials (SRM 1643e, Natural Water-Trace Elements, and SRM 2669, Arsenic Species in Frozen Human Urine) and through spike recovery tests. The values of percentage recovery for SRM 2669 were 90.7 % for As(III), 99.8 % for As(V), 93.6 % for DMA, and 85.9 % for MMA. A good correlation was also found between the certified (60.45 mu gL(-1)) and determined (59.00 mu gL(-1)) values for SRM 1643e. Moreover, the speciation capability of the method was demonstrated on various natural waters and biological fluids.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    A Facile Method for Boosting the Graphitic Carbon Nitride's Photocatalytic Activity Based on 0d/2d S-Scheme Heterojunction Nanocomposite Architecture
    (Elsevier, 2024) Kahraman, Zeynep; Kartal, Uğur; Gent, Aziz; Alp, Emre
    Graphitic carbon nitride (g-C 3 N 4 ) has received significant interest as a metal -free photocatalyst. The S -scheme photocatalytic system has great potential to improve the charge separation in semiconductor photocatalysts. In this study, we have fabricated non-toxic and low-cost photocatalytic nanocomposites of 0D/2D S -scheme heterojunction composed of iron oxide and graphitic carbon nitride by a facile method. The developed facile method provides a sustainable way with a high atom economy to further enhance the photocatalytic performance of exfoliated g-C 3 N 4 . The 0D -iron oxide/2D-C 3 N 4 exhibited nearly 10 times better than bulk g-C 3 N 4 and almost 60 % better than exfoliated g-C 3 N 4 under simulated solar light irradiation. The experimental results demonstrated that the effective charge -carrier mechanism led to an improved generation of reactive oxygen species (ROSs), resulting in an impressive photocatalytic performance. A serial photocatalytic test was also conducted to understand photocatalytic reaction mechanisms with various scavengers.
  • Article
    Citation - WoS: 13
    Citation - Scopus: 17
    Machine Learning-Assisted Prediction of the Toxicity of Silver Nanoparticles: a Meta-Analysis
    (Springer, 2023) Bilgi, Eyüp; Öksel Karakuş, Ceyda
    Silver nanoparticles are likely to be more dangerous than other forms of silver due to the intracellular release of silver ions upon dissolution and the formation of mixed ion-containing complexes. Such concerns have resulted in an ever-growing pile of scientific evaluations addressing the safety aspects of nanosilver with widely varying methodological approaches. The substantial differences in the conduct/design of nanotoxicity screening have led to the generation of conflicting findings that may be accurate in their narrative but fail to provide a complete picture. One strategy to maximize the use of individual risk assessments with potentially biased estimates of toxicological effects is to homogenize results across several studies and to increase the generalizability and human relevance of their findings. Here, we collected a large pool of data (n=162 independent studies) on the cytotoxicity of nanosilver and unrevealed potential triggers of toxicity. Two different machine learning approaches, decision tree (DT) and artificial neural network (ANN), were primarily employed to develop models that can predict the cytotoxic potential of nanosilver based on material- and assay-related parameters. Other machine learning algorithms (logistic regression, Gaussian Naive Bayes, k-nearest neighbor, and random forest classifiers) were also applied. Among several attributes compared, exposure concentration, duration, zeta potential, particle size, and coating were found to have the most substantial impact on nanotoxicity, with biomolecule- and microorganism-assisted surface modifications having the most beneficial and detrimental effects on cell survival, respectively. Such machine learning-assisted efforts are critical to developing commercially viable and safe nanosilver-containing products in the ever-expanding nanobiomaterial market.